In this paper, we present a Mixed-variable, Multi-Objective Bayesian Optimization (MixMOBO) algorithm that is a generalized framework that can deal with these types of problems in the small data setting and that can optimize a noisy black-box function with a small number of function calls. Using a GA on a mixed variable surrogate model in a multi-objective setting allows us to work with most of the published modified kernels that were developed for mixed-variable problems. We present a modified Hedge strategy for acquisition functions, Hedge Multi-Objective (HedgeMO). The strategy has batch updates that work in a multi-objective setting for which the regret bounds presented by [1]} hold. We also propose a new acquisition function (AF), Stochastic Monte-Carlo (SMC), which performs well for categorical variables [2]}.
In summary, the main contributions of our work are as follows:
